"""
The :mod:`sklearn.compose._column_transformer` module implements utilities
to work with heterogeneous data and to apply different transformers to
different columns.
"""
# Author: Andreas Mueller
#         Joris Van den Bossche
# License: BSD
import warnings
from itertools import chain

import numbers
import numpy as np
from scipy import sparse
from joblib import Parallel, delayed

from ..base import clone, TransformerMixin
from ..pipeline import _fit_transform_one, _transform_one, _name_estimators
from ..preprocessing import FunctionTransformer
from ..utils import Bunch
from ..utils import _safe_indexing
from ..utils import _get_column_indices
from ..utils import _determine_key_type
from ..utils.metaestimators import _BaseComposition
from ..utils.validation import check_array, check_is_fitted
from ..utils.validation import _deprecate_positional_args


__all__ = [
    'ColumnTransformer', 'make_column_transformer', 'make_column_selector'
]


_ERR_MSG_1DCOLUMN = ("1D data passed to a transformer that expects 2D data. "
                     "Try to specify the column selection as a list of one "
                     "item instead of a scalar.")


class ColumnTransformer(TransformerMixin, _BaseComposition):
    """Applies transformers to columns of an array or pandas DataFrame.

    This estimator allows different columns or column subsets of the input
    to be transformed separately and the features generated by each transformer
    will be concatenated to form a single feature space.
    This is useful for heterogeneous or columnar data, to combine several
    feature extraction mechanisms or transformations into a single transformer.

    Read more in the :ref:`User Guide <column_transformer>`.

    .. versionadded:: 0.20

    Parameters
    ----------
    transformers : list of tuples
        List of (name, transformer, columns) tuples specifying the
        transformer objects to be applied to subsets of the data.

        name : str
            Like in Pipeline and FeatureUnion, this allows the transformer and
            its parameters to be set using ``set_params`` and searched in grid
            search.
        transformer : {'drop', 'passthrough'} or estimator
            Estimator must support :term:`fit` and :term:`transform`.
            Special-cased strings 'drop' and 'passthrough' are accepted as
            well, to indicate to drop the columns or to pass them through
            untransformed, respectively.
        columns :  str, array-like of str, int, array-like of int, \
                array-like of bool, slice or callable
            Indexes the data on its second axis. Integers are interpreted as
            positional columns, while strings can reference DataFrame columns
            by name.  A scalar string or int should be used where
            ``transformer`` expects X to be a 1d array-like (vector),
            otherwise a 2d array will be passed to the transformer.
            A callable is passed the input data `X` and can return any of the
            above. To select multiple columns by name or dtype, you can use
            :obj:`make_column_selector`.

    remainder : {'drop', 'passthrough'} or estimator, default='drop'
        By default, only the specified columns in `transformers` are
        transformed and combined in the output, and the non-specified
        columns are dropped. (default of ``'drop'``).
        By specifying ``remainder='passthrough'``, all remaining columns that
        were not specified in `transformers` will be automatically passed
        through. This subset of columns is concatenated with the output of
        the transformers.
        By setting ``remainder`` to be an estimator, the remaining
        non-specified columns will use the ``remainder`` estimator. The
        estimator must support :term:`fit` and :term:`transform`.
        Note that using this feature requires that the DataFrame columns
        input at :term:`fit` and :term:`transform` have identical order.

    sparse_threshold : float, default=0.3
        If the output of the different transformers contains sparse matrices,
        these will be stacked as a sparse matrix if the overall density is
        lower than this value. Use ``sparse_threshold=0`` to always return
        dense.  When the transformed output consists of all dense data, the
        stacked result will be dense, and this keyword will be ignored.

    n_jobs : int, default=None
        Number of jobs to run in parallel.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

    transformer_weights : dict, default=None
        Multiplicative weights for features per transformer. The output of the
        transformer is multiplied by these weights. Keys are transformer names,
        values the weights.

    verbose : bool, default=False
        If True, the time elapsed while fitting each transformer will be
        printed as it is completed.

    Attributes
    ----------
    transformers_ : list
        The collection of fitted transformers as tuples of
        (name, fitted_transformer, column). `fitted_transformer` can be an
        estimator, 'drop', or 'passthrough'. In case there were no columns
        selected, this will be the unfitted transformer.
        If there are remaining columns, the final element is a tuple of the
        form:
        ('remainder', transformer, remaining_columns) corresponding to the
        ``remainder`` parameter. If there are remaining columns, then
        ``len(transformers_)==len(transformers)+1``, otherwise
        ``len(transformers_)==len(transformers)``.

    named_transformers_ : :class:`~sklearn.utils.Bunch`
        Read-only attribute to access any transformer by given name.
        Keys are transformer names and values are the fitted transformer
        objects.

    sparse_output_ : bool
        Boolean flag indicating whether the output of ``transform`` is a
        sparse matrix or a dense numpy array, which depends on the output
        of the individual transformers and the `sparse_threshold` keyword.

    Notes
    -----
    The order of the columns in the transformed feature matrix follows the
    order of how the columns are specified in the `transformers` list.
    Columns of the original feature matrix that are not specified are
    dropped from the resulting transformed feature matrix, unless specified
    in the `passthrough` keyword. Those columns specified with `passthrough`
    are added at the right to the output of the transformers.

    See also
    --------
    sklearn.compose.make_column_transformer : convenience function for
        combining the outputs of multiple transformer objects applied to
        column subsets of the original feature space.
    sklearn.compose.make_column_selector : convenience function for selecting
        columns based on datatype or the columns name with a regex pattern.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.compose import ColumnTransformer
    >>> from sklearn.preprocessing import Normalizer
    >>> ct = ColumnTransformer(
    ...     [("norm1", Normalizer(norm='l1'), [0, 1]),
    ...      ("norm2", Normalizer(norm='l1'), slice(2, 4))])
    >>> X = np.array([[0., 1., 2., 2.],
    ...               [1., 1., 0., 1.]])
    >>> # Normalizer scales each row of X to unit norm. A separate scaling
    >>> # is applied for the two first and two last elements of each
    >>> # row independently.
    >>> ct.fit_transform(X)
    array([[0. , 1. , 0.5, 0.5],
           [0.5, 0.5, 0. , 1. ]])

    """
    _required_parameters = ['transformers']

    @_deprecate_positional_args
    def __init__(self,
                 transformers, *,
                 remainder='drop',
                 sparse_threshold=0.3,
                 n_jobs=None,
                 transformer_weights=None,
                 verbose=False):
        self.transformers = transformers
        self.remainder = remainder
        self.sparse_threshold = sparse_threshold
        self.n_jobs = n_jobs
        self.transformer_weights = transformer_weights
        self.verbose = verbose

    @property
    def _transformers(self):
        """
        Internal list of transformer only containing the name and
        transformers, dropping the columns. This is for the implementation
        of get_params via BaseComposition._get_params which expects lists
        of tuples of len 2.
        """
        return [(name, trans) for name, trans, _ in self.transformers]

    @_transformers.setter
    def _transformers(self, value):
        self.transformers = [
            (name, trans, col) for ((name, trans), (_, _, col))
            in zip(value, self.transformers)]

    def get_params(self, deep=True):
        """Get parameters for this estimator.

        Parameters
        ----------
        deep : bool, default=True
            If True, will return the parameters for this estimator and
            contained subobjects that are estimators.

        Returns
        -------
        params : dict
            Parameter names mapped to their values.
        """
        return self._get_params('_transformers', deep=deep)

    def set_params(self, **kwargs):
        """Set the parameters of this estimator.

        Valid parameter keys can be listed with ``get_params()``.

        Returns
        -------
        self
        """
        self._set_params('_transformers', **kwargs)
        return self

    def _iter(self, fitted=False, replace_strings=False):
        """
        Generate (name, trans, column, weight) tuples.

        If fitted=True, use the fitted transformers, else use the
        user specified transformers updated with converted column names
        and potentially appended with transformer for remainder.

        """
        if fitted:
            transformers = self.transformers_
        else:
            # interleave the validated column specifiers
            transformers = [
                (name, trans, column) for (name, trans, _), column
                in zip(self.transformers, self._columns)
            ]
            # add transformer tuple for remainder
            if self._remainder[2] is not None:
                transformers = chain(transformers, [self._remainder])
        get_weight = (self.transformer_weights or {}).get

        for name, trans, column in transformers:
            if replace_strings:
                # replace 'passthrough' with identity transformer and
                # skip in case of 'drop'
                if trans == 'passthrough':
                    trans = FunctionTransformer(
                        accept_sparse=True, check_inverse=False
                    )
                elif trans == 'drop':
                    continue
                elif _is_empty_column_selection(column):
                    continue

            yield (name, trans, column, get_weight(name))

    def _validate_transformers(self):
        if not self.transformers:
            return

        names, transformers, _ = zip(*self.transformers)

        # validate names
        self._validate_names(names)

        # validate estimators
        for t in transformers:
            if t in ('drop', 'passthrough'):
                continue
            if (not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not
                    hasattr(t, "transform")):
                raise TypeError("All estimators should implement fit and "
                                "transform, or can be 'drop' or 'passthrough' "
                                "specifiers. '%s' (type %s) doesn't." %
                                (t, type(t)))

    def _validate_column_callables(self, X):
        """
        Converts callable column specifications.
        """
        columns = []
        for _, _, column in self.transformers:
            if callable(column):
                column = column(X)
            columns.append(column)
        self._columns = columns

    def _validate_remainder(self, X):
        """
        Validates ``remainder`` and defines ``_remainder`` targeting
        the remaining columns.
        """
        is_transformer = ((hasattr(self.remainder, "fit")
                           or hasattr(self.remainder, "fit_transform"))
                          and hasattr(self.remainder, "transform"))
        if (self.remainder not in ('drop', 'passthrough')
                and not is_transformer):
            raise ValueError(
                "The remainder keyword needs to be one of 'drop', "
                "'passthrough', or estimator. '%s' was passed instead" %
                self.remainder)

        # Make it possible to check for reordered named columns on transform
        if (hasattr(X, 'columns') and
                any(_determine_key_type(cols) == 'str'
                    for cols in self._columns)):
            self._df_columns = X.columns

        self._n_features = X.shape[1]
        cols = []
        for columns in self._columns:
            cols.extend(_get_column_indices(X, columns))
        remaining_idx = list(set(range(self._n_features)) - set(cols))
        remaining_idx = sorted(remaining_idx) or None

        self._remainder = ('remainder', self.remainder, remaining_idx)

    @property
    def named_transformers_(self):
        """Access the fitted transformer by name.

        Read-only attribute to access any transformer by given name.
        Keys are transformer names and values are the fitted transformer
        objects.

        """
        # Use Bunch object to improve autocomplete
        return Bunch(**{name: trans for name, trans, _
                        in self.transformers_})

    def get_feature_names(self):
        """Get feature names from all transformers.

        Returns
        -------
        feature_names : list of strings
            Names of the features produced by transform.
        """
        check_is_fitted(self)
        feature_names = []
        for name, trans, column, _ in self._iter(fitted=True):
            if trans == 'drop' or (
                    hasattr(column, '__len__') and not len(column)):
                continue
            elif trans == 'passthrough':
                raise NotImplementedError(
                    "get_feature_names is not yet supported when using "
                    "a 'passthrough' transformer.")
            elif not hasattr(trans, 'get_feature_names'):
                raise AttributeError("Transformer %s (type %s) does not "
                                     "provide get_feature_names."
                                     % (str(name), type(trans).__name__))
            feature_names.extend([name + "__" + f for f in
                                  trans.get_feature_names()])
        return feature_names

    def _update_fitted_transformers(self, transformers):
        # transformers are fitted; excludes 'drop' cases
        fitted_transformers = iter(transformers)
        transformers_ = []

        for name, old, column, _ in self._iter():
            if old == 'drop':
                trans = 'drop'
            elif old == 'passthrough':
                # FunctionTransformer is present in list of transformers,
                # so get next transformer, but save original string
                next(fitted_transformers)
                trans = 'passthrough'
            elif _is_empty_column_selection(column):
                trans = old
            else:
                trans = next(fitted_transformers)
            transformers_.append((name, trans, column))

        # sanity check that transformers is exhausted
        assert not list(fitted_transformers)
        self.transformers_ = transformers_

    def _validate_output(self, result):
        """
        Ensure that the output of each transformer is 2D. Otherwise
        hstack can raise an error or produce incorrect results.
        """
        names = [name for name, _, _, _ in self._iter(fitted=True,
                                                      replace_strings=True)]
        for Xs, name in zip(result, names):
            if not getattr(Xs, 'ndim', 0) == 2:
                raise ValueError(
                    "The output of the '{0}' transformer should be 2D (scipy "
                    "matrix, array, or pandas DataFrame).".format(name))

    def _validate_features(self, n_features, feature_names):
        """Ensures feature counts and names are the same during fit and
        transform.

        TODO: It should raise an error from v0.24
        """

        if ((self._feature_names_in is None or feature_names is None)
                and self._n_features == n_features):
            return

        neg_col_present = np.any([_is_negative_indexing(col)
                                  for col in self._columns])
        if neg_col_present and self._n_features != n_features:
            raise RuntimeError("At least one negative column was used to "
                               "indicate columns, and the new data's number "
                               "of columns does not match the data given "
                               "during fit. "
                               "Please make sure the data during fit and "
                               "transform have the same number of columns.")

        if (self._n_features != n_features or
                np.any(self._feature_names_in != np.asarray(feature_names))):
            warnings.warn("Given feature/column names or counts do not match "
                          "the ones for the data given during fit. This will "
                          "fail from v0.24.",
                          FutureWarning)

    def _log_message(self, name, idx, total):
        if not self.verbose:
            return None
        return '(%d of %d) Processing %s' % (idx, total, name)

    def _fit_transform(self, X, y, func, fitted=False):
        """
        Private function to fit and/or transform on demand.

        Return value (transformers and/or transformed X data) depends
        on the passed function.
        ``fitted=True`` ensures the fitted transformers are used.
        """
        transformers = list(
            self._iter(fitted=fitted, replace_strings=True))
        try:
            return Parallel(n_jobs=self.n_jobs)(
                delayed(func)(
                    transformer=clone(trans) if not fitted else trans,
                    X=_safe_indexing(X, column, axis=1),
                    y=y,
                    weight=weight,
                    message_clsname='ColumnTransformer',
                    message=self._log_message(name, idx, len(transformers)))
                for idx, (name, trans, column, weight) in enumerate(
                        self._iter(fitted=fitted, replace_strings=True), 1))
        except ValueError as e:
            if "Expected 2D array, got 1D array instead" in str(e):
                raise ValueError(_ERR_MSG_1DCOLUMN)
            else:
                raise

    def fit(self, X, y=None):
        """Fit all transformers using X.

        Parameters
        ----------
        X : {array-like, dataframe} of shape (n_samples, n_features)
            Input data, of which specified subsets are used to fit the
            transformers.

        y : array-like of shape (n_samples,...), default=None
            Targets for supervised learning.

        Returns
        -------
        self : ColumnTransformer
            This estimator

        """
        # we use fit_transform to make sure to set sparse_output_ (for which we
        # need the transformed data) to have consistent output type in predict
        self.fit_transform(X, y=y)
        return self

    def fit_transform(self, X, y=None):
        """Fit all transformers, transform the data and concatenate results.

        Parameters
        ----------
        X : {array-like, dataframe} of shape (n_samples, n_features)
            Input data, of which specified subsets are used to fit the
            transformers.

        y : array-like of shape (n_samples,), default=None
            Targets for supervised learning.

        Returns
        -------
        X_t : {array-like, sparse matrix} of \
                shape (n_samples, sum_n_components)
            hstack of results of transformers. sum_n_components is the
            sum of n_components (output dimension) over transformers. If
            any result is a sparse matrix, everything will be converted to
            sparse matrices.

        """
        # TODO: this should be `feature_names_in_` when we start having it
        if hasattr(X, "columns"):
            self._feature_names_in = np.asarray(X.columns)
        else:
            self._feature_names_in = None
        X = _check_X(X)
        # set n_features_in_ attribute
        self._check_n_features(X, reset=True)
        self._validate_transformers()
        self._validate_column_callables(X)
        self._validate_remainder(X)

        result = self._fit_transform(X, y, _fit_transform_one)

        if not result:
            self._update_fitted_transformers([])
            # All transformers are None
            return np.zeros((X.shape[0], 0))

        Xs, transformers = zip(*result)

        # determine if concatenated output will be sparse or not
        if any(sparse.issparse(X) for X in Xs):
            nnz = sum(X.nnz if sparse.issparse(X) else X.size for X in Xs)
            total = sum(X.shape[0] * X.shape[1] if sparse.issparse(X)
                        else X.size for X in Xs)
            density = nnz / total
            self.sparse_output_ = density < self.sparse_threshold
        else:
            self.sparse_output_ = False

        self._update_fitted_transformers(transformers)
        self._validate_output(Xs)

        return self._hstack(list(Xs))

    def transform(self, X):
        """Transform X separately by each transformer, concatenate results.

        Parameters
        ----------
        X : {array-like, dataframe} of shape (n_samples, n_features)
            The data to be transformed by subset.

        Returns
        -------
        X_t : {array-like, sparse matrix} of \
                shape (n_samples, sum_n_components)
            hstack of results of transformers. sum_n_components is the
            sum of n_components (output dimension) over transformers. If
            any result is a sparse matrix, everything will be converted to
            sparse matrices.

        """
        check_is_fitted(self)
        X = _check_X(X)
        if hasattr(X, "columns"):
            X_feature_names = np.asarray(X.columns)
        else:
            X_feature_names = None

        if self._n_features > X.shape[1]:
            raise ValueError('Number of features of the input must be equal '
                             'to or greater than that of the fitted '
                             'transformer. Transformer n_features is {0} '
                             'and input n_features is {1}.'
                             .format(self._n_features, X.shape[1]))

        # No column reordering allowed for named cols combined with remainder
        # TODO: remove this mechanism in 0.24, once we enforce strict column
        # name order and count. See #14237 for details.
        if (self._remainder[2] is not None and
                hasattr(self, '_df_columns') and
                hasattr(X, 'columns')):
            n_cols_fit = len(self._df_columns)
            n_cols_transform = len(X.columns)
            if (n_cols_transform >= n_cols_fit and
                    any(X.columns[:n_cols_fit] != self._df_columns)):
                raise ValueError('Column ordering must be equal for fit '
                                 'and for transform when using the '
                                 'remainder keyword')

        # TODO: also call _check_n_features(reset=False) in 0.24
        self._validate_features(X.shape[1], X_feature_names)
        Xs = self._fit_transform(X, None, _transform_one, fitted=True)
        self._validate_output(Xs)

        if not Xs:
            # All transformers are None
            return np.zeros((X.shape[0], 0))

        return self._hstack(list(Xs))

    def _hstack(self, Xs):
        """Stacks Xs horizontally.

        This allows subclasses to control the stacking behavior, while reusing
        everything else from ColumnTransformer.

        Parameters
        ----------
        Xs : list of {array-like, sparse matrix, dataframe}
        """
        if self.sparse_output_:
            try:
                # since all columns should be numeric before stacking them
                # in a sparse matrix, `check_array` is used for the
                # dtype conversion if necessary.
                converted_Xs = [check_array(X,
                                            accept_sparse=True,
                                            force_all_finite=False)
                                for X in Xs]
            except ValueError:
                raise ValueError("For a sparse output, all columns should"
                                 " be a numeric or convertible to a numeric.")

            return sparse.hstack(converted_Xs).tocsr()
        else:
            Xs = [f.toarray() if sparse.issparse(f) else f for f in Xs]
            return np.hstack(Xs)


def _check_X(X):
    """Use check_array only on lists and other non-array-likes / sparse"""
    if hasattr(X, '__array__') or sparse.issparse(X):
        return X
    return check_array(X, force_all_finite='allow-nan', dtype=np.object)


def _is_empty_column_selection(column):
    """
    Return True if the column selection is empty (empty list or all-False
    boolean array).

    """
    if hasattr(column, 'dtype') and np.issubdtype(column.dtype, np.bool_):
        return not column.any()
    elif hasattr(column, '__len__'):
        return len(column) == 0
    else:
        return False


def _get_transformer_list(estimators):
    """
    Construct (name, trans, column) tuples from list

    """
    transformers, columns = zip(*estimators)
    names, _ = zip(*_name_estimators(transformers))

    transformer_list = list(zip(names, transformers, columns))
    return transformer_list


def make_column_transformer(*transformers, **kwargs):
    """Construct a ColumnTransformer from the given transformers.

    This is a shorthand for the ColumnTransformer constructor; it does not
    require, and does not permit, naming the transformers. Instead, they will
    be given names automatically based on their types. It also does not allow
    weighting with ``transformer_weights``.

    Read more in the :ref:`User Guide <make_column_transformer>`.

    Parameters
    ----------
    *transformers : tuples
        Tuples of the form (transformer, columns) specifying the
        transformer objects to be applied to subsets of the data.

        transformer : {'drop', 'passthrough'} or estimator
            Estimator must support :term:`fit` and :term:`transform`.
            Special-cased strings 'drop' and 'passthrough' are accepted as
            well, to indicate to drop the columns or to pass them through
            untransformed, respectively.
        columns : str,  array-like of str, int, array-like of int, slice, \
                array-like of bool or callable
            Indexes the data on its second axis. Integers are interpreted as
            positional columns, while strings can reference DataFrame columns
            by name. A scalar string or int should be used where
            ``transformer`` expects X to be a 1d array-like (vector),
            otherwise a 2d array will be passed to the transformer.
            A callable is passed the input data `X` and can return any of the
            above. To select multiple columns by name or dtype, you can use
            :obj:`make_column_selector`.

    remainder : {'drop', 'passthrough'} or estimator, default='drop'
        By default, only the specified columns in `transformers` are
        transformed and combined in the output, and the non-specified
        columns are dropped. (default of ``'drop'``).
        By specifying ``remainder='passthrough'``, all remaining columns that
        were not specified in `transformers` will be automatically passed
        through. This subset of columns is concatenated with the output of
        the transformers.
        By setting ``remainder`` to be an estimator, the remaining
        non-specified columns will use the ``remainder`` estimator. The
        estimator must support :term:`fit` and :term:`transform`.

    sparse_threshold : float, default=0.3
        If the transformed output consists of a mix of sparse and dense data,
        it will be stacked as a sparse matrix if the density is lower than this
        value. Use ``sparse_threshold=0`` to always return dense.
        When the transformed output consists of all sparse or all dense data,
        the stacked result will be sparse or dense, respectively, and this
        keyword will be ignored.

    n_jobs : int, default=None
        Number of jobs to run in parallel.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

    verbose : bool, default=False
        If True, the time elapsed while fitting each transformer will be
        printed as it is completed.

    Returns
    -------
    ct : ColumnTransformer

    See also
    --------
    sklearn.compose.ColumnTransformer : Class that allows combining the
        outputs of multiple transformer objects used on column subsets
        of the data into a single feature space.

    Examples
    --------
    >>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
    >>> from sklearn.compose import make_column_transformer
    >>> make_column_transformer(
    ...     (StandardScaler(), ['numerical_column']),
    ...     (OneHotEncoder(), ['categorical_column']))
    ColumnTransformer(transformers=[('standardscaler', StandardScaler(...),
                                     ['numerical_column']),
                                    ('onehotencoder', OneHotEncoder(...),
                                     ['categorical_column'])])

    """
    # transformer_weights keyword is not passed through because the user
    # would need to know the automatically generated names of the transformers
    n_jobs = kwargs.pop('n_jobs', None)
    remainder = kwargs.pop('remainder', 'drop')
    sparse_threshold = kwargs.pop('sparse_threshold', 0.3)
    verbose = kwargs.pop('verbose', False)
    if kwargs:
        raise TypeError('Unknown keyword arguments: "{}"'
                        .format(list(kwargs.keys())[0]))
    transformer_list = _get_transformer_list(transformers)
    return ColumnTransformer(transformer_list, n_jobs=n_jobs,
                             remainder=remainder,
                             sparse_threshold=sparse_threshold,
                             verbose=verbose)


def _is_negative_indexing(key):
    # TODO: remove in v0.24
    def is_neg(x): return isinstance(x, numbers.Integral) and x < 0
    if isinstance(key, slice):
        return is_neg(key.start) or is_neg(key.stop)
    elif _determine_key_type(key) == 'int':
        return np.any(np.asarray(key) < 0)
    return False


class make_column_selector:
    """Create a callable to select columns to be used with
    :class:`ColumnTransformer`.

    :func:`make_column_selector` can select columns based on datatype or the
    columns name with a regex. When using multiple selection criteria, **all**
    criteria must match for a column to be selected.

    Parameters
    ----------
    pattern : str, default=None
        Name of columns containing this regex pattern will be included. If
        None, column selection will not be selected based on pattern.

    dtype_include : column dtype or list of column dtypes, default=None
        A selection of dtypes to include. For more details, see
        :meth:`pandas.DataFrame.select_dtypes`.

    dtype_exclude : column dtype or list of column dtypes, default=None
        A selection of dtypes to exclude. For more details, see
        :meth:`pandas.DataFrame.select_dtypes`.

    Returns
    -------
    selector : callable
        Callable for column selection to be used by a
        :class:`ColumnTransformer`.

    See also
    --------
    sklearn.compose.ColumnTransformer : Class that allows combining the
        outputs of multiple transformer objects used on column subsets
        of the data into a single feature space.

    Examples
    --------
    >>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
    >>> from sklearn.compose import make_column_transformer
    >>> from sklearn.compose import make_column_selector
    >>> import pandas as pd  # doctest: +SKIP
    >>> X = pd.DataFrame({'city': ['London', 'London', 'Paris', 'Sallisaw'],
    ...                   'rating': [5, 3, 4, 5]})  # doctest: +SKIP
    >>> ct = make_column_transformer(
    ...       (StandardScaler(),
    ...        make_column_selector(dtype_include=np.number)),  # rating
    ...       (OneHotEncoder(),
    ...        make_column_selector(dtype_include=object)))  # city
    >>> ct.fit_transform(X)  # doctest: +SKIP
    array([[ 0.90453403,  1.        ,  0.        ,  0.        ],
           [-1.50755672,  1.        ,  0.        ,  0.        ],
           [-0.30151134,  0.        ,  1.        ,  0.        ],
           [ 0.90453403,  0.        ,  0.        ,  1.        ]])
    """
    @_deprecate_positional_args
    def __init__(self, pattern=None, *, dtype_include=None,
                 dtype_exclude=None):
        self.pattern = pattern
        self.dtype_include = dtype_include
        self.dtype_exclude = dtype_exclude

    def __call__(self, df):
        if not hasattr(df, 'iloc'):
            raise ValueError("make_column_selector can only be applied to "
                             "pandas dataframes")
        df_row = df.iloc[:1]
        if self.dtype_include is not None or self.dtype_exclude is not None:
            df_row = df_row.select_dtypes(include=self.dtype_include,
                                          exclude=self.dtype_exclude)
        cols = df_row.columns
        if self.pattern is not None:
            cols = cols[cols.str.contains(self.pattern, regex=True)]
        return cols.tolist()
